Measurement of Industrial Robot Trajectories With Reorientations
Why this work is in the frame
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Bibliographic record
Abstract
The increasing use of industrial robots in different applications raises the demand of the robots performance. This yields to the request for reliable data for the performance characteristics of industrial robots. In this paper a measurement solution for dynamic industrial robot motion measurement with a significant amount of reorientations is presented. The proposed method uses an optical coordinate measurement system with light emitting diodes (LEDs) as active markers. The reorientations of the robots tool increases the difficulty of temporary occlusion of markers, disappearance of markers and reappearance of previously hidden markers. An automated marker registration system based on quality evaluation for single LED measurements has been developed to allow flexible maker setups and decrease the possibility of measurement errors. To interpret the measurement data, an additional error metric for the according ISO standard for measuring robot motion is proposed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it